论文标题

虚拟现实(VR)作为消费者光学解决方案的测试台:机器学习方法(GBR)在模拟渐进式添加镜头(PALS)扭曲下的视觉舒适度

Virtual reality (VR) as a testing bench for consumer optical solutions: A machine learning approach (GBR) to visual comfort under simulated progressive addition lenses (PALS) distortions

论文作者

García, Miguel García, Sauer, Yannick, Watson, Tamara, Wahl, Siegfried

论文摘要

几十年来,制造商一直试图减少或消除制造过程中渐进式镜头表面上出现的光差。除了付出的每一项努力之外,考虑到镜片的制造方式,其中一些扭曲是不可避免的,实际上,散光出现在表面上,不能完全消除,或者在整个镜头上的功率变化固有的固有的位置。一些长老会第一次佩戴这些镜头时可能会引起某些不适,其中一部分可能永远不会适应。开发,原型制作,测试和将这些镜片投入市场的成本通常以零售价反映在市场上。这项研究旨在测试虚拟现实的可行性,以测试客户对这些镜头的满意度,甚至在将它们纳入生产之前。 VR提供了一个受控的环境,可以分别分析影响渐进透镜舒适度的不同参数,例如扭曲,图像位移或光学模糊。在这项研究中,将重点放在扭曲和图像位移上,而不是考虑模糊。使用内置的眼动器记录行为变化(头和眼动)。在高度扭曲的透镜模拟存在下,参与者的不满。此外,将梯度提升回归器安装在数据中,因此可以揭示不适的预测因素,并且可以预测无需进行其他测量的评分。

For decades, manufacturers have attempted to reduce or eliminate the optical aberrations that appear on the progressive addition lens' surfaces during manufacturing. Besides every effort made, some of these distortions are inevitable given how lenses are fabricated, where in fact, astigmatism appears on the surface and cannot be entirely removed or where non-uniform magnification becomes inherent to the power change across the lens. Some presbyopes may refer to certain discomfort when wearing these lenses for the first time, and a subset of them might never adapt. Developing, prototyping, testing and purveying those lenses into the market come at a cost, which is usually reflected in the retail price. This study aims to test the feasibility of virtual reality for testing customers' satisfaction with these lenses, even before getting them onto production. VR offers a controlled environment where different parameters affecting progressive lens comforts, such as distortions, image displacement or optical blurring, can be analysed separately. In this study, the focus was set on the distortions and image displacement, not taking blur into account. Behavioural changes (head and eye movements) were recorded using the built-in eye tracker. Participants were significantly more displeased in the presence of highly distorted lens simulations. In addition, a gradient boosting regressor was fitted to the data, so predictors of discomfort could be unveiled, and ratings could be predicted without performing additional measurements.

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